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One of the numerous drawbacks of existing systems for wrong-way driver detection (WWD) is that they require installation and maintenance of expensive sensor networks. More importantly, they fail to leverage on the growing number of traffic surveillance camera networks. Approaching wrong way driver detection from a computer vision standpoint is a rather intricate one if not well thought out. As such, recent methods which explored alternative deep learning approach for solving this problem have been shown to exhibit a high rate of false detection and consider very limited settings e.g. exit ramps. In this paper, we propose a more sophisticated computer vision framework to address the shortcomings of existing systems while also leveraging on existing preinstalled large-scale camera infrastructure to achieve real-time WWD detection with high precision. The proposed framework combines four modules working collaboratively to deliver desired results. This includes: (i) a Flow Detection Module which is initialized to determine the correct direction of flow by momentarily observing the traffic; (ii) a state-of-the-art object detection algorithm, in this case YOLOv5, for detecting all objects of interest from each frame; (iii) a sophisticated centroid-based object tracker coupled with Hungarian matching algorithm for efficiently tracking objects of interest; and (iv) a wrong way flagging module to flag vehicles moving opposite to a lane’s computed flow direction as they enter and exit the camera’s field of view. The Hungarian algorithm ensures that each object of interest is assigned a unique ID which not only reinforces tracking efficiency of the object tracker, but also provides traffic count capability. Tracking paths are compared against computed direction of flow to instantly detect wrong way driving. The proposed architecture achieves state-of-the-art performance with high True Positive Rate and low false detections. One of the several benefits of the proposed method is that it could potentially be integrated into the department of transport (DOT) surveillance system to significantly reduce the cognitive load pressure on traffic control agents who are overwhelmed by the large number of video feeds they are tasked to monitor in real-time. Alerts generated from this system could help mitigate such issues.
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